1. Modeling Reading Development From First Grade Text Michael W. Harm, CMU Mark S. Seidenberg, Wisconsin/Madison

2. Why Construct Computational Models? Can complement empirical studies Allow manipulation of multiple factors that would be prohibitively difficult in a classroom setting We can “look under the hood” and see what kinds of internal representations are formed during learning

3. Computational Models of Reading Have a long history… –Rumelhart & McClelland 1981, Seidenberg & McClelland 1989, Plaut & Hinton, 1991, Coltheart et. al, 1993, Plaut et. al 1996, Zorzi 1999, etc. Historically, have been aimed at explaining adult performance –Architecture of the reading system –Acquired dyslexia (due to brain damage) Have traditionally not examined issues of reading development

4. Modeling Development Was Difficult With Earlier Models Earlier models did not learn about phonology –But phonology is important in learning to read! Sampled large corpus of (adult) text in toto –Did not allow for manipulation of actual sequence or nature of word exposures –Prohibited examination of effects of different basals, interventions

5. The Harm & Seidenberg 1999 Model of Reading Phonological Knowledge Begin by modeling pre- literate phonological knowledge that children have Can vary the strength and consistency of this knowledge … and simulate the different degrees of phonological ability children bring to bear learning to read

6. Reading Uses this Phonological Knowledge Phonological Knowledge Text The model must map print onto this structured phonological representation to read aloud The nature of the phono representations influences what is learned during reading Core result: the phonologically impaired model learns differently

7. Two New Applications Simulation of effects of different instructional basals Simulation of an reading intervention scheme We have applied the Harm & Seidenberg 1999 model to two novel applications:

8. Simulating Effect of Different Reading Basals Some early reading texts are more tuned to overlap in spelling/sound Others emphasize variety in text; exposure to wider range of words Q: How does this interact with reading impairments?

9. Results: Nonword Reading Basal 1: More tuned to spelling/sound correspondences Basal 2: Less systematic text For both normal and impaired model, Basal 1 better than Basal 2.

10. Results: Word Reading For normal models, large effect of basal For impaired models, floor effect: smaller difference

11. Summary: Simulating Effects of Basals We can explore the impact of reading materials, and differential effects on normal and impaired reading Holds promise for more sophisticated explorations/manipulations … and direct ties to more fine grained properties of basals (see other talks in this session)

12. Simulating Reading Interventions There is extensive evidence that one cause of poor reading development is a phonological impairment However, interventions targeted at auditory phonology generally are not very effective Interventions aimed at spelling/sound representations have greater success Why?

13. Analysis of the Model Spelling Phonology The core impairment is in phonology … But leads to poor representations between spelling and sound So effective interventions must target the relationship between spelling and sound

14. The McCandliss et al. Intervention Use “lessons” based on the Beck word- building scheme Break words apart when errors are made –Emphasizes componential structure of words –… and its relation to components of sound

15. Performed Simulation of this Intervention Used phonologically impaired simulation from Harm & Seidenberg 1999 Simulated intervention using actual items from lessons Brought about improvements in nonword reading Analyzed internal representations of words

16. Normal and Impaired Simulation Normal SimulationImpaired Simulation

17. Representations Cluster Better Due to Intervention Remediated Simulation

18. Conclusions Computational simulations are now poised to explore more detailed aspects of children’s experience –With reading basals –And with interventions Opens up a promising new line of research linking behavioral experimentation with computational analysis

19. With thanks to... Mark S. Seidenberg Bruce McCandliss

20. fin